{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "import tensorflow.contrib.layers as layers\n",
    "from sklearn import datasets\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection  import train_test_split\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import r2_score\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "import keras\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Data\n",
    "boston = datasets.load_boston()\n",
    "df = pd.DataFrame(boston.data, columns=boston.feature_names)\n",
    "df['target'] = boston.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.593761</td>\n",
       "      <td>11.363636</td>\n",
       "      <td>11.136779</td>\n",
       "      <td>0.069170</td>\n",
       "      <td>0.554695</td>\n",
       "      <td>6.284634</td>\n",
       "      <td>68.574901</td>\n",
       "      <td>3.795043</td>\n",
       "      <td>9.549407</td>\n",
       "      <td>408.237154</td>\n",
       "      <td>18.455534</td>\n",
       "      <td>356.674032</td>\n",
       "      <td>12.653063</td>\n",
       "      <td>22.532806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.596783</td>\n",
       "      <td>23.322453</td>\n",
       "      <td>6.860353</td>\n",
       "      <td>0.253994</td>\n",
       "      <td>0.115878</td>\n",
       "      <td>0.702617</td>\n",
       "      <td>28.148861</td>\n",
       "      <td>2.105710</td>\n",
       "      <td>8.707259</td>\n",
       "      <td>168.537116</td>\n",
       "      <td>2.164946</td>\n",
       "      <td>91.294864</td>\n",
       "      <td>7.141062</td>\n",
       "      <td>9.197104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.006320</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.460000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.385000</td>\n",
       "      <td>3.561000</td>\n",
       "      <td>2.900000</td>\n",
       "      <td>1.129600</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>187.000000</td>\n",
       "      <td>12.600000</td>\n",
       "      <td>0.320000</td>\n",
       "      <td>1.730000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.082045</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.190000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.449000</td>\n",
       "      <td>5.885500</td>\n",
       "      <td>45.025000</td>\n",
       "      <td>2.100175</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>279.000000</td>\n",
       "      <td>17.400000</td>\n",
       "      <td>375.377500</td>\n",
       "      <td>6.950000</td>\n",
       "      <td>17.025000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.256510</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.690000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.538000</td>\n",
       "      <td>6.208500</td>\n",
       "      <td>77.500000</td>\n",
       "      <td>3.207450</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>330.000000</td>\n",
       "      <td>19.050000</td>\n",
       "      <td>391.440000</td>\n",
       "      <td>11.360000</td>\n",
       "      <td>21.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.647423</td>\n",
       "      <td>12.500000</td>\n",
       "      <td>18.100000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.624000</td>\n",
       "      <td>6.623500</td>\n",
       "      <td>94.075000</td>\n",
       "      <td>5.188425</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>666.000000</td>\n",
       "      <td>20.200000</td>\n",
       "      <td>396.225000</td>\n",
       "      <td>16.955000</td>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>88.976200</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>27.740000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.871000</td>\n",
       "      <td>8.780000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>12.126500</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>711.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>396.900000</td>\n",
       "      <td>37.970000</td>\n",
       "      <td>50.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             CRIM          ZN       INDUS        CHAS         NOX          RM  \\\n",
       "count  506.000000  506.000000  506.000000  506.000000  506.000000  506.000000   \n",
       "mean     3.593761   11.363636   11.136779    0.069170    0.554695    6.284634   \n",
       "std      8.596783   23.322453    6.860353    0.253994    0.115878    0.702617   \n",
       "min      0.006320    0.000000    0.460000    0.000000    0.385000    3.561000   \n",
       "25%      0.082045    0.000000    5.190000    0.000000    0.449000    5.885500   \n",
       "50%      0.256510    0.000000    9.690000    0.000000    0.538000    6.208500   \n",
       "75%      3.647423   12.500000   18.100000    0.000000    0.624000    6.623500   \n",
       "max     88.976200  100.000000   27.740000    1.000000    0.871000    8.780000   \n",
       "\n",
       "              AGE         DIS         RAD         TAX     PTRATIO           B  \\\n",
       "count  506.000000  506.000000  506.000000  506.000000  506.000000  506.000000   \n",
       "mean    68.574901    3.795043    9.549407  408.237154   18.455534  356.674032   \n",
       "std     28.148861    2.105710    8.707259  168.537116    2.164946   91.294864   \n",
       "min      2.900000    1.129600    1.000000  187.000000   12.600000    0.320000   \n",
       "25%     45.025000    2.100175    4.000000  279.000000   17.400000  375.377500   \n",
       "50%     77.500000    3.207450    5.000000  330.000000   19.050000  391.440000   \n",
       "75%     94.075000    5.188425   24.000000  666.000000   20.200000  396.225000   \n",
       "max    100.000000   12.126500   24.000000  711.000000   22.000000  396.900000   \n",
       "\n",
       "            LSTAT      target  \n",
       "count  506.000000  506.000000  \n",
       "mean    12.653063   22.532806  \n",
       "std      7.141062    9.197104  \n",
       "min      1.730000    5.000000  \n",
       "25%      6.950000   17.025000  \n",
       "50%     11.360000   21.200000  \n",
       "75%     16.955000   25.000000  \n",
       "max     37.970000   50.000000  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Understanding Data\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAArAAAAI6CAYAAADfb3sIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1b61f4e5630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting correlation color map\n",
    "_ , ax = plt.subplots( figsize =( 12 , 10 ) )\n",
    "\n",
    "corr = df.corr(method='pearson')\n",
    "cmap = sns.diverging_palette( 220 , 10 , as_cmap = True )\n",
    "_ = sns.heatmap(\n",
    "    corr, \n",
    "    cmap = cmap,\n",
    "    square=True, \n",
    "    cbar_kws={ 'shrink' : .9 }, \n",
    "    ax=ax, \n",
    "    annot = True, \n",
    "    annot_kws = { 'fontsize' : 12 })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Create Test Train Split\n",
    "X_train, X_test, y_train, y_test = train_test_split(df [['RM', 'LSTAT', 'PTRATIO']], df[['target']], test_size=0.3, random_state=0)\n",
    "\n",
    "# Normalize data\n",
    "X_train = np.array(MinMaxScaler().fit_transform(X_train))   # Need to convert dataframe to np.array for use with Keras \n",
    "y_train = np.array(MinMaxScaler().fit_transform(y_train))\n",
    "\n",
    "X_test =  np.array(MinMaxScaler().fit_transform(X_test))\n",
    "y_test =  np.array(MinMaxScaler().fit_transform(y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Network Parameters\n",
    "m = len(X_train)\n",
    "n = 3   # Number of features\n",
    "n_hidden = 20  # Number of hidden neurons\n",
    "\n",
    "\n",
    "# Hyperparameters\n",
    "batch = 20\n",
    "eta = 0.01\n",
    "max_epoch = 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_1 (Dense)              (None, 20)                80        \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 1)                 21        \n",
      "=================================================================\n",
      "Total params: 101\n",
      "Trainable params: 101\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Build Model\n",
    "model = Sequential()\n",
    "model.add(Dense(n_hidden, input_dim=n, activation='relu'))\n",
    "model.add(Dense(1, activation='sigmoid'))\n",
    "model.summary()   # Summarize the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Compile model\n",
    "model.compile(loss='mean_squared_error', optimizer='adam')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 354 samples, validate on 152 samples\n",
      "Epoch 1/100\n",
      "354/354 [==============================] - 1s - loss: 0.0930 - val_loss: 0.0911\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 2/100\n",
      "354/354 [==============================] - 0s - loss: 0.0722 - val_loss: 0.0702\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 3/100\n",
      "354/354 [==============================] - 0s - loss: 0.0564 - val_loss: 0.0558\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 4/100\n",
      "354/354 [==============================] - 0s - loss: 0.0465 - val_loss: 0.0464\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 5/100\n",
      "354/354 [==============================] - 0s - loss: 0.0400 - val_loss: 0.0414\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 6/100\n",
      "354/354 [==============================] - 0s - loss: 0.0366 - val_loss: 0.0380\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 7/100\n",
      "354/354 [==============================] - 0s - loss: 0.0344 - val_loss: 0.0359\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 8/100\n",
      "354/354 [==============================] - 0s - loss: 0.0328 - val_loss: 0.0344\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 9/100\n",
      "354/354 [==============================] - 0s - loss: 0.0314 - val_loss: 0.0332\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 10/100\n",
      "354/354 [==============================] - 0s - loss: 0.0302 - val_loss: 0.0320\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 11/100\n",
      "354/354 [==============================] - 0s - loss: 0.0290 - val_loss: 0.0310\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 12/100\n",
      "354/354 [==============================] - 0s - loss: 0.0277 - val_loss: 0.0300\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 13/100\n",
      "354/354 [==============================] - 0s - loss: 0.0265 - val_loss: 0.0290\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 14/100\n",
      "354/354 [==============================] - 0s - loss: 0.0253 - val_loss: 0.0279\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 15/100\n",
      "354/354 [==============================] - 0s - loss: 0.0241 - val_loss: 0.0269\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 16/100\n",
      "354/354 [==============================] - 0s - loss: 0.0229 - val_loss: 0.0259\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 17/100\n",
      "354/354 [==============================] - 0s - loss: 0.0217 - val_loss: 0.0250\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 18/100\n",
      "354/354 [==============================] - 0s - loss: 0.0206 - val_loss: 0.0241\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 19/100\n",
      "354/354 [==============================] - 0s - loss: 0.0196 - val_loss: 0.0232\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 20/100\n",
      "354/354 [==============================] - 0s - loss: 0.0186 - val_loss: 0.0224\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 21/100\n",
      "354/354 [==============================] - 0s - loss: 0.0177 - val_loss: 0.0217\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 22/100\n",
      "354/354 [==============================] - 0s - loss: 0.0169 - val_loss: 0.0210\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 23/100\n",
      "354/354 [==============================] - 0s - loss: 0.0161 - val_loss: 0.0204\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 24/100\n",
      "354/354 [==============================] - 0s - loss: 0.0154 - val_loss: 0.0198\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 25/100\n",
      "354/354 [==============================] - 0s - loss: 0.0148 - val_loss: 0.0193\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 26/100\n",
      "354/354 [==============================] - 0s - loss: 0.0142 - val_loss: 0.0189\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 27/100\n",
      "354/354 [==============================] - 0s - loss: 0.0137 - val_loss: 0.0185\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 28/100\n",
      "354/354 [==============================] - 0s - loss: 0.0133 - val_loss: 0.0181\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 29/100\n",
      "354/354 [==============================] - 0s - loss: 0.0129 - val_loss: 0.0178\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 30/100\n",
      "354/354 [==============================] - 0s - loss: 0.0126 - val_loss: 0.0176\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 31/100\n",
      "354/354 [==============================] - 0s - loss: 0.0123 - val_loss: 0.0173\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 32/100\n",
      "354/354 [==============================] - 0s - loss: 0.0121 - val_loss: 0.0171\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 33/100\n",
      "354/354 [==============================] - 0s - loss: 0.0118 - val_loss: 0.0170\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 34/100\n",
      "354/354 [==============================] - 0s - loss: 0.0116 - val_loss: 0.0168\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 35/100\n",
      "354/354 [==============================] - 0s - loss: 0.0114 - val_loss: 0.0166\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 36/100\n",
      "354/354 [==============================] - 0s - loss: 0.0113 - val_loss: 0.0165\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 37/100\n",
      "354/354 [==============================] - 0s - loss: 0.0111 - val_loss: 0.0163\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 38/100\n",
      "354/354 [==============================] - 0s - loss: 0.0110 - val_loss: 0.0162\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 39/100\n",
      "354/354 [==============================] - 0s - loss: 0.0109 - val_loss: 0.0161\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 40/100\n",
      "354/354 [==============================] - 0s - loss: 0.0108 - val_loss: 0.0160\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 41/100\n",
      "354/354 [==============================] - 0s - loss: 0.0107 - val_loss: 0.0159\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 42/100\n",
      "354/354 [==============================] - 0s - loss: 0.0106 - val_loss: 0.0158\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 43/100\n",
      "354/354 [==============================] - 0s - loss: 0.0106 - val_loss: 0.0157\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 44/100\n",
      "354/354 [==============================] - 0s - loss: 0.0105 - val_loss: 0.0156\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 45/100\n",
      "354/354 [==============================] - 0s - loss: 0.0104 - val_loss: 0.0156\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 46/100\n",
      "354/354 [==============================] - 0s - loss: 0.0104 - val_loss: 0.0156\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 47/100\n",
      "354/354 [==============================] - 0s - loss: 0.0103 - val_loss: 0.0155\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 48/100\n",
      "354/354 [==============================] - 0s - loss: 0.0103 - val_loss: 0.0154\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 49/100\n",
      "354/354 [==============================] - 0s - loss: 0.0102 - val_loss: 0.0154\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 50/100\n",
      "354/354 [==============================] - 0s - loss: 0.0102 - val_loss: 0.0153\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 51/100\n",
      "354/354 [==============================] - 0s - loss: 0.0102 - val_loss: 0.0152\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 52/100\n",
      "354/354 [==============================] - 0s - loss: 0.0101 - val_loss: 0.0152\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 53/100\n",
      "354/354 [==============================] - 0s - loss: 0.0102 - val_loss: 0.0152\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 54/100\n",
      "354/354 [==============================] - 0s - loss: 0.0101 - val_loss: 0.0151\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 55/100\n",
      "354/354 [==============================] - 0s - loss: 0.0101 - val_loss: 0.0152\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 56/100\n",
      "354/354 [==============================] - 0s - loss: 0.0100 - val_loss: 0.0151\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 57/100\n",
      "354/354 [==============================] - 0s - loss: 0.0100 - val_loss: 0.0150\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 58/100\n",
      "354/354 [==============================] - 0s - loss: 0.0100 - val_loss: 0.0150\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 59/100\n",
      "354/354 [==============================] - 0s - loss: 0.0099 - val_loss: 0.0149\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 60/100\n",
      "354/354 [==============================] - 0s - loss: 0.0100 - val_loss: 0.0149\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 61/100\n",
      "354/354 [==============================] - 0s - loss: 0.0099 - val_loss: 0.0149\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 62/100\n",
      "354/354 [==============================] - 0s - loss: 0.0099 - val_loss: 0.0149\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 63/100\n",
      "354/354 [==============================] - 0s - loss: 0.0099 - val_loss: 0.0147\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 64/100\n",
      "354/354 [==============================] - 0s - loss: 0.0098 - val_loss: 0.0148\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 65/100\n",
      "354/354 [==============================] - 0s - loss: 0.0098 - val_loss: 0.0147\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 66/100\n",
      "354/354 [==============================] - 0s - loss: 0.0098 - val_loss: 0.0147\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 67/100\n",
      "354/354 [==============================] - 0s - loss: 0.0098 - val_loss: 0.0146\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 68/100\n",
      "354/354 [==============================] - 0s - loss: 0.0097 - val_loss: 0.0147\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 69/100\n",
      "354/354 [==============================] - 0s - loss: 0.0097 - val_loss: 0.0146\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 70/100\n",
      "354/354 [==============================] - 0s - loss: 0.0097 - val_loss: 0.0146\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 71/100\n",
      "354/354 [==============================] - 0s - loss: 0.0098 - val_loss: 0.0146\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 72/100\n",
      "354/354 [==============================] - 0s - loss: 0.0096 - val_loss: 0.0145\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 73/100\n",
      "354/354 [==============================] - 0s - loss: 0.0097 - val_loss: 0.0145\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 74/100\n",
      "354/354 [==============================] - 0s - loss: 0.0096 - val_loss: 0.0145\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 75/100\n",
      "354/354 [==============================] - 0s - loss: 0.0096 - val_loss: 0.0145\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 76/100\n",
      "354/354 [==============================] - 0s - loss: 0.0096 - val_loss: 0.0144\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 77/100\n",
      "354/354 [==============================] - 0s - loss: 0.0096 - val_loss: 0.0144\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 78/100\n",
      "354/354 [==============================] - 0s - loss: 0.0095 - val_loss: 0.0144\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 79/100\n",
      "354/354 [==============================] - 0s - loss: 0.0095 - val_loss: 0.0144\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 80/100\n",
      "354/354 [==============================] - 0s - loss: 0.0095 - val_loss: 0.0144\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 81/100\n",
      "354/354 [==============================] - 0s - loss: 0.0095 - val_loss: 0.0144\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 82/100\n",
      "354/354 [==============================] - 0s - loss: 0.0095 - val_loss: 0.0143\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 83/100\n",
      "354/354 [==============================] - 0s - loss: 0.0095 - val_loss: 0.0143\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 84/100\n",
      "354/354 [==============================] - 0s - loss: 0.0094 - val_loss: 0.0143\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 85/100\n",
      "354/354 [==============================] - 0s - loss: 0.0094 - val_loss: 0.0143\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 86/100\n",
      "354/354 [==============================] - 0s - loss: 0.0094 - val_loss: 0.0143\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 87/100\n",
      "354/354 [==============================] - 0s - loss: 0.0094 - val_loss: 0.0143\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 88/100\n",
      "354/354 [==============================] - 0s - loss: 0.0093 - val_loss: 0.0142\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 89/100\n",
      "354/354 [==============================] - 0s - loss: 0.0093 - val_loss: 0.0142\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 90/100\n",
      "354/354 [==============================] - 0s - loss: 0.0093 - val_loss: 0.0142\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 91/100\n",
      "354/354 [==============================] - 0s - loss: 0.0093 - val_loss: 0.0142\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 92/100\n",
      "354/354 [==============================] - 0s - loss: 0.0093 - val_loss: 0.0142\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 93/100\n",
      "354/354 [==============================] - 0s - loss: 0.0093 - val_loss: 0.0141\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 94/100\n",
      "354/354 [==============================] - 0s - loss: 0.0093 - val_loss: 0.0141\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 95/100\n",
      "354/354 [==============================] - 0s - loss: 0.0093 - val_loss: 0.0141\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 96/100\n",
      "354/354 [==============================] - 0s - loss: 0.0092 - val_loss: 0.0141\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 97/100\n",
      "354/354 [==============================] - 0s - loss: 0.0093 - val_loss: 0.0141\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 98/100\n",
      "354/354 [==============================] - 0s - loss: 0.0092 - val_loss: 0.0141\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 99/100\n",
      "354/354 [==============================] - 0s - loss: 0.0092 - val_loss: 0.0140\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 100/100\n",
      "354/354 [==============================] - 0s - loss: 0.0092 - val_loss: 0.0140\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x1b620019da0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Fit the model\n",
    "model.fit(X_train, y_train, validation_data=(X_test, y_test),epochs=max_epoch, batch_size=batch, verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Performance Metrics R2 : 0.667622, RMSE : 0.014039\n"
     ]
    }
   ],
   "source": [
    "y_test_pred = model.predict(X_test)\n",
    "y_train_pred = model.predict(X_train)\n",
    "r2 = r2_score( y_test, y_test_pred )\n",
    "rmse = mean_squared_error( y_test, y_test_pred  )\n",
    "print( \"Performance Metrics R2 : {0:f}, RMSE : {1:f}\".format( r2, rmse ) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1b6200f07f0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYwAAAEWCAYAAAB1xKBvAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYVFaCfHAlpoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1b620040cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(1)\n",
    "plt.subplot(211)\n",
    "plt.scatter(y_test, y_test_pred)\n",
    "plt.title('Actual vs Predicted House Price Test Data')\n",
    "plt.ylabel('Estimated House Price')\n",
    "plt.subplots_adjust(hspace=.5)\n",
    "plt.subplot(212)\n",
    "plt.scatter(y_train, y_train_pred, color= 'red')\n",
    "plt.title('Actual vs Predicted House Price Train Data')\n",
    "plt.xlabel('Actual House Price')\n",
    "plt.ylabel('Estimated House Price')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:tensorflow]",
   "language": "python",
   "name": "conda-env-tensorflow-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
